Overview:

The Face Recognition Attendance System is an AI-powered application that automates the process of marking attendance using facial recognition technology.
Instead of traditional manual attendance (signatures, ID cards, or roll calls), this system captures the student’s or employee’s face through a webcam or mobile camera, recognizes it using a trained Machine Learning model, and marks attendance automatically in the database.

This reduces proxy attendance, saves time, and ensures accurate record-keeping in schools, colleges, or offices.

Objectives:

• To automate attendance marking using computer vision and AI.

• To minimize human errors and eliminate proxy attendance.

• To create a real-time attendance tracking system accessible to admin and users.

Key Features:

1. Face Detection & Recognition: Uses ML algorithms to detect and match faces with the existing database.

2. Live Camera Integration: Captures face through webcam or mobile camera in real-time.

3. Automatic Attendance Marking: Marks attendance once the face is successfully recognized.

4. Daily & Monthly Reports: Generates attendance summaries for students/employees.

5. AI Model Training: Admin can train the system with new faces to update the recognition dataset.

6. Secure Login System: Separate logins for admin, teacher, and student.

7. Real-time Status: Displays present/absent status instantly.

8. Admin Dashboard: Manage users, track attendance percentage, and export reports to Excel/PDF.

9. Web/Mobile Access: Accessible through both mobile and web interfaces.

10. Database Integration: All attendance data stored securely in the backend database.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Node.js / PHP / Python (Flask or Django preferred)

• Database: MySQL / MongoDB

• Machine Learning & Computer Vision:

  • Libraries: OpenCV, NumPy, dlib, face_recognition (Python)

  • Algorithms: CNN (Convolutional Neural Networks) for face matching

• Hardware: Webcam or IP Camera

Workflow:

1. Registration Phase:

   • Admin uploads or captures face images of users (students/employees).

   • The ML model trains and stores unique face encodings in the database.

2. Attendance Phase:

   • The camera scans faces in real-time.

   • The system compares detected faces with stored encodings.

   • If a match is found → marks attendance automatically.

3. Report Generation:

   • Attendance records are updated in the database.

   • Admin can view daily or monthly attendance reports.